IEEE ICASSP 2022

Human-centric applications like automated screening of chronic Cardio-Vascular Diseases (CVDs) for remote and primary care is of immense global importance. Atrial Fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with significant mortality and morbidity. The benchmark deep learning-based approach from Andrew N.G.'s team at Stanford [1] and our own domain knowledge augmented signal processing features based machine learning approach (global winner of Physionet Challenge 2017) [2], have performed robustly at near expert-level classification for Atrial Fibrillation (AF) detection from single-lead Electrocardiogram (ECG). To create real impact, these models need to be run on wearable and implantable edge devices like smartwatches, smart bands, implantable loop recorders (ILR) for short-time and long-time ECG screening. However, such models are computationally expensive with more than 100 MB model memory size, whereas these edge devices are run by tiny low-power microcontroller units, often limited to sub-MB memory size having strict power budget due to limited battery life. This necessitates for elegant model size reduction approaches without penalizing the performance. In our earlier work, it is shown that Knowledge Distillation (KD)-based piecewise linear approach is capable of trimming down the memory requirement of the DL model by nearly 150x with more than 5000x reduction in computational complexity (and hence power consumption) for simple ECG analytics task [3], with less than 1% loss in inferencing accuracy . However it is seen that for such approaches, there is severe performance degradation issue in more complex analytics tasks (like AF detection) that uses sophisticated base DL model like [1] - the inferencing accuracy loss can go more than 20%. We introduce a more powerful iterative pruning-based model size reduction, like Lottery Ticket Hypothesis (LTH), that starts from a complex DL model like [1] and can elegantly prune and find the sub-network that is compact in parameter space (100x approx.) yet with less than 1% loss in inferencing accuracy [4]. In future, we plan to extend this work further via hybrid KD-LTH approaches for spectrum of human-centric applications on Tiny Edge Devices like wearables and implantable - some of the applications we are working on include other cardiac, musculoskeletal and neurological disorders, geriatric care, human behavior sensing for Neuromarketing, Brain-computer Interface/Human Robot Interaction (BCI/HRI) etc.

References - [1] Andrew N.G. et. al. “Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network,” Nature Medicine, 2019. [2] A. Pal et. al., “Detection of atrial fibrillation and other abnormal rhythms from ECG using a multi-layer classifier architecture,” Physiological Measurements, June, 2019. [3] A. Pal et. al., "Resource Constrained CVD Classification Using Single Lead ECG On Wearable and Implantable Devices," IEEE EMBC 2021. [4] A. Pal et. al., "LTH-ECG: Lottery Ticket Hypothesis-based Deep Learning Model Compression for Atrial Fibrillation Detection from Single Lead ECG On Wearable and Implantable Devices," IEEE EMBC 2022, Submitted.

Biography

Arpan Pal has more than 29 years of experience in the area of Intelligent Sensing, Signal Processing &AI, Edge Computing and Affective Computing. Currently, as Chief Scientist and Research Area Head, Embedded Devices and Intelligent Systems, TCS Research, he is working in the areas of Connected Health, Smart Manufacturing and Remote Sensing. He is on the editorial board of notable journals like ACM Transactions on Embedded Systems, Springer Nature Journal on Computer Science and is on the TPC of notable conferences like ICASSP and EUSIPCO. He has filed 165+ patents (out of which 85+ granted in different geographies) and has published 140+ papers and book chapters in reputed conferences and journals. He has also authored two books – one on IoT and another on Digital Twins in Manufacturing. He is on the governing/review/advisory board of some of the Indian Government organizations like CSIR, MeitY, Educational Institutions like IIT, IIIT and Technology Incubation centers like TIH. Prior to joining Tata Consultancy Services (TCS), Arpan had worked for DRDO, India as Scientist for Missile Seeker Systems and in Rebeca Technologies (erstwhile Macmet Interactive Technologies) from as their Head of Real-time Systems. He is a B.Tech and M. Tech from IIT, Kharagpur, India and PhD. from Aalborg University, Denmark. Home Page - https://www.tcs.com/embedded-devices-intelligent-systems Linked In - http://in.linkedin.com/in/arpanpal Google Scholar - http://scholar.google.co.in/citations?user=hkKS-xsAAAAJ&hl=en Orcid - https://orcid.org/0000-0001-9101-8051